Innovative Projects Realized

Explore thousands of successful projects resulting from collaboration between organizations and post-secondary talent.

29670 Completed Projects

2811
AB
4990
BC
801
MB
663
NL
825
SK
8841
ON
9197
QC
95
PE
568
NB
1088
NS

Projects by Category

Développement d’algorithmes de moindres carrés non-linéaires sous con-traintes appliquées pour la prévision de la demande en électricité

La prévision de la demande d’électricité à court terme est un des enjeux majeurs de l’exercice des activités d’Hydro-Québec. Cette tâche requiert de calibrer les paramètres de modèles de prévision complexes par rapport à un grand nombre de données. Or, un des systèmes de calibration utilisé quotidiennement éprouve des difficultés d’adaptation face à la nouvelle conjoncture climatique et sociétale. Un des axes de recherche pour faire face à ces enjeux porte sur la modernisation de l’algorithme d’optimisation intervenant dans le processus de calibration. Le but de ce projet est alors d’implémenter deux méthodes d’op-timisation modernes et adaptées aux nouveaux besoins d’Hydro-Québec, afin d’améliorer les performances de l’outil de pré-vision à court terme actuellement en place.

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Faculty Supervisor:

Fabian Bastin

Student:

Partner:

Hydro-Quebec

Discipline:

Engineering

Sector:

Technology; Green/Alternative Energy; Energy and Utilities

University:

Université de Montréal

Program:

Accelerate

Finetuning an Embedding Model for RAG in the Construction of Minority Languages

This project seeks to bring a PhD student from Yonsei University, South Korea, to Concordia University for research focused on developing an AI-powered automated construction site monitoring technology. Building upon the applicant’s expertise gained during their studies in South Korea, we aim to establish a Large Multimodal Model capable of generalizing object recognition, meeting Level 2 proficiency as per the Artificial General Intelligence standard. With the host advisor’s AI expertise and access to computing resources like GPU-embedded desktops, the PDF is expected to successfully achieve its objectives within the stipulated timeframe. This pioneering initiative toward a generalized large-scale AI model represents a significant advancement in automated construction monitoring, promising to elevate automation levels in Canadian construction industries. Collaboration through these proposed plans will not only enrich the PDF’s research and professional network but also open new research avenues for Concordia to explore.

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Faculty Supervisor:

Jong Won Ma

Student:

Partner:

Yonsei University

Discipline:

Engineering

Sector:

Education

University:

Concordia University

Program:

Globalink Research Award

L’intelligence artificielle pour analyser l’acceptabilité des projets de ressources naturelles

En utilisant des modèles de Traitement Automatique des Langues (TAL), notre objectif est d’analyser de manière approfondie les articles de presse pour extraire des informations cruciales sur l’acceptabilité sociale des projets liés aux ressources naturelles. Nous nous concentrons sur l’identification des parties prenantes impliquées, ainsi que sur l’identification des facteurs clés de succès et des obstacles potentiels pour ces projets. Cette analyse permettra de mieux comprendre les perceptions et les préoccupations de la société, offrant ainsi aux décideurs une base solide pour l’élaboration de stratégies visant à renforcer l’acceptabilité sociale de ces projets et à favoriser leur succès à long terme

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Faculty Supervisor:

Jérôme Deschênes

Student:

Partner:

École nationale des sciences de l'informatique

Discipline:

Computer science

Sector:

Education

University:

Université du Québec en Abitibi-Témiscamingue

Program:

Globalink Research Award

Développement d’un procédé de fabrication d’un récepteur CPV

Ce stage s’inscrit dans un projet de recherche qui a pour objectif le développement d’un récepteur CPV avec un réseau dense de cellules solaires, en utilisant une architecture basée sur le concept de FOWLP (Fan-Out Level Packaging). Les cellules à concentration (CPV) présentent l’efficacité la plus élevée parmi toutes les technologies photovoltaïques, et le concept FOWLP est de plus en plus adopté dans l’industrie des semi-conducteurs pour, entre autres, réduire les coûts de fabrication des dispositifs. Cette architecture innovante vise à réduire les pertes d’ombrage en diminuant les zones “mortes” des cellules solaires de matériaux III-V, favorisant ainsi la conversion de photons en courant électrique. Grâce à un procédé d’assemblage du module photovoltaïque plus efficace par rapport au procédé classique, les coûts de fabrication seront réduits, rendant ainsi cette technologie plus compétitive. Le travail de ce stage se concentrera sur les aspects de fabrication et de packaging des récepteurs CPV.

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Faculty Supervisor:

Gwenaëlle Hamon;Jonathan Naisby

Student:

Partner:

Université Paris-Saclay

Discipline:

Engineering

Sector:

Education

University:

Université de Sherbrooke

Program:

Globalink Research Award

Data fitting to Dengue fever

The objective of the research project is to study an efficient and fast way to calculate values of some parameters which are hard to be estimated in nature in relation to dengue, they are: the biting rate, probability that a host to get infected, probability that a vector to get infected. These parameters are needed to estimate the “Basic Reproduction Number”, in a very simple way this number is defined as the expected number of secondary cases of a disease produced by a single infection in a completely susceptible population.

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Faculty Supervisor:

Jianhong Wu

Student:

Partner:

Universidade de São Paulo

Discipline:

Mathematics

Sector:

University:

York University

Program:

Globalink Research Award

Machine Learning-based Approaches for Channel Estimation and Spectrum Sharing

Using simplified language understandable to a layperson; provide a general, one-paragraph description of the proposed research project to be undertaken by the intern(s) as well as the expected benefit to the partner organization. (100–150 words)
The proposed research is focused on developing machine learning and deep learning-based approaches to improve the operation of 5G networks in two main sub-project areas: channel reconstruction and spectrum sharing. In the first sub-project, we propose a methodology to reconstruct the wireless channel based solely on limited downlink CSI metrics. The purpose of this study is to recreate channels experienced during over-the-air testing by extracting their key channel characteristics. These characteristics will be used to create a similar simulated channel for further testing. This will improve the simulated channel’s accuracy, making it resemble the real-world scenario in which the CSI was collected. In the second sub-project, i.e., spectrum sharing, we propose the use of different Deep Reinforcement Learning (DRL) algorithms to develop novel and innovative approaches for optimal and distributed spectrum sharing and optimizing the operation of RIS-assisted UAV networks and RIS-assisted m-MIMO networks for 5GB systems.
The outcomes of the project will include models for channel reconstruction as well as new approaches for spectrum sharing. The impact of this project is an increase in the data rates and significant reduction in delays over 5G networks, which will improve the services provided to end users and clients over such networks.

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Faculty Supervisor:

Gabriel Wainer

Student:

Partner:

Ericsson Canada Inc (Ottawa, ON)

Discipline:

Engineering

Sector:

Information and cultural industries; Manufacturing; Professional, scientific and technical services

University:

Carleton University

Program:

Accelerate

Employment of alternative in vivo cancer models for oncology preclinical investigations

The clinical translation of innovative oncology treatments can be boosted by the establishment of alternative in vivo models based on the chick chorioallantoic membrane. In this project, a biomodel based on commercial cancer cell lines, such as hepatocellular carcinoma, will be established and characterized by standard biological assays. The biomodels will be employed for the efficacy evaluation of novel treatment approaches including hybrid nanomaterials.

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Faculty Supervisor:

Gang Zheng

Student:

Partner:

Università Degli Studi Di Genova

Discipline:

Life Sciences

Sector:

Biotechnology; Health and Related Sciences & Technology; Nanotechnology

University:

University of Toronto

Program:

Globalink Research Award

Developing and Delivering Resources to support the Transition to Sustainable Craft in Unama’ki: Cape Breton

This research will explore ways the craft sector in Unama’ki: Cape Breton can adapt to become more sustainable. That exploration will inform the development of visually engaging resources and reports that will be shared broadly with artists, craftspeople, and others in the craft and culture sector.?This will include a toolkit, map of resources for craftspeople, an academic report, and formal presentation materials.
This work is part of our ongoing Sustainable Craft program, in which we focus on craft materials, practice(s), and the life cycle of craft products. We are engaging in solutions-oriented work to identify small-scale, grassroots, and community-based approaches to support the sustainable development of the Island’s craft and creative sector.
This project will empower the organization to better support craftspeople and professional artists in transitioning to more durable, sustainable, and ethical practices. It will also enable the organization to support other craft and related organizations to develop more sustainable operations and support sustainable craft in their communities.

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Faculty Supervisor:

Jennifer Green;Kate Kish

Student:

Partner:

Cape Breton Centre for Craft & Design, Cape Breton School of Crafts

Discipline:

Sociology

Sector:

Education

University:

Cape Breton University; Nova Scotia College of Art & Design University

Program:

Accelerate

Automated Anomaly Detection in Energy Consumption Data using Data Mining and Machine Learning Techniques

The Ontario government recently mandated electric and natural gas utilities provide easy access to energy usage data so consumers can access energy-saving services in a competitive market. However, there has been no analysis of how well utilities have implemented Green Button. More specifically, are regulatory requirements being met, and is data accurate, complete, consistent with other available sources? This lack of scrutiny imperils Ontario’s efforts to decarbonize its economy because poor-quality data can inhibit clean energy solutions such as energy management, demand response, and virtual power plants. This project is the first to evaluate Ontario’s energy usage, billing, and account data quantitatively/qualitatively from utilities to benchmark performance. This project seeks to minimize resource use and time by employing a mixed-method machine learning approach, i.e., semi-supervised learning. The findings from this study will apply to other Canadian provinces.

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Faculty Supervisor:

Francis Palma

Student:

Partner:

Screaming Power

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of New Brunswick

Program:

Accelerate

Developing a physically-based, geomorphic model to evaluate the probability of pipeline exposure at stream crossings

There are currently tens of thousands of locations throughout North America where pipelines cross stream channels. At these locations, instability of the stream bed and banks poses a serious risk to pipeline infrastructure, and has the potential to cause environmental harm. The objective of this internship is to improve the numerical models used to monitor the risk of channel instability associated with a range of flood magnitudes; currently the models don’t quantify the likelihood of the channel bed becoming mobile, and fail to consider the reach-scale processes of channel stability in a mechanistic manner. The model will be driven with data already collected at thousands of site surveys, and used as a screening tool to determine which sites are most vulnerable to pipe exposure, and therefore require more detailed field assessment. Based on the input variables required in the model, we will also develop a standardized field protocol to improve and focus data collection at future site assessments.

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Faculty Supervisor:

Brett Eaton

Student:

Partner:

BGC Engineering Inc (BC)

Discipline:

Physics

Sector:

Construction and infrastructure; Professional, scientific and technical services

University:

The University of British Columbia

Program:

Accelerate

Multi-Modal Kernels: Prompting MM-LLMs to Yield Controllable Semantic Similarity Functions

“THIS IS A GENERIC TEXT PUT IN PLACE AS THERE WAS NO PROJECT OVERVIEW”

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Faculty Supervisor:

Reihaneh Rabbany;Adriana Romero Soriano

Student:

Partner:

ServiceNow Canada

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University

Program:

Accelerate

Utilisation de l’IA et du Big Data dans un environnement IoT pour les appareils urbains intelligents

La croissance rapide des villes et la demande croissante de services urbains efficaces et durables posent des défis importants aux gestionnaires municipaux. Les appareils urbains intelligents, tels que les lampadaires, les poubelles, les bornes de recharge, etc., sont des éléments clés pour améliorer la qualité de vie des citoyens et optimiser les ressources urbaines. Cependant, ces appareils génèrent une grande quantité de données qui doivent être traitées, analysées et utilisées de manière intelligente pour soutenir la prise de décision et l’adaptation dynamique des services. Le projet de recherche vise à exploiter les données des appareils urbains intelligents dans l’Internet des objets (IoT) en utilisant l’intelligence artificielle (IA) et le Big Data. Cette recherche proposerait des méthodes pour collecter, analyser et utiliser efficacement ces données, en utilisant des techniques d’IA telles que l’apprentissage automatique. L’objectif est d’optimiser les services urbains et d’améliorer la qualité de vie des citoyens ainsi qu’à fournir des informations utiles aux décideurs et aux utilisateurs finaux, en s’appuyant sur une solution infonuagique, contribuant ainsi à l’atteinte des objectifs de développement durable (SDG) numéro 11 : Villes et Communautés Durables.

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Faculty Supervisor:

Julien Gascon-Samson

Student:

Partner:

Humanity-Tech

Discipline:

Computer science

Sector:

Information and cultural industries

University:

École de technologie supérieure

Program:

Accelerate

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